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2025
How can consumers without credit history benefit from the use of information processing and machine learning tools by financial institutions? (2025)Information processing & management, 62(2). Article 103972. van Braak, B., Osterrieder, J. R. & Machado, M. R.https://doi.org/10.1016/j.ipm.2024.103972Modeling commodity price co-movement: building on traditional time series models and exploring applications of machine learning algorithms (2025)Decisions in economics and finance. Article 109283 (E-pub ahead of print/First online). Kozian, L. L., Machado, M. R. & Osterrieder, J. R.https://doi.org/10.1007/s10203-025-00512-1Predicting retail customers' distress in the finance industry: An early warning system approach (2025)Journal of Retailing and Consumer Services, 82. Article 104101. Beltman, J., Machado, M. R. & Osterrieder, J. R.https://doi.org/10.1016/j.jretconser.2024.104101
2024
Green AI in the Finance Industry: Exploring the Impact of Feature Engineering on the Accuracy and Computational Time of Machine Learning Models (2024)Applied Soft Computing, 167. Article 112343. Machado, M., Asadi, A., William R. de Souza, R. & Ugulino, W.https://doi.org/10.1016/j.asoc.2024.112343How can Artificial Intelligence (AI) be used to manage Customer Lifetime Value (CLV)—A systematic literature review (2024)International Journal of Information Management Data Insights, 4(2). Article 100279. Firmansyah, E. B., Machado, M. R. & Moreira, J. L. R.https://doi.org/10.1016/j.jjimei.2024.100279How can artificial intelligence help customer intelligence for credit portfolio management? A systematic literature review (2024)International Journal of Information Management Data Insights, 4(2). Article 100234. Amato, A., Osterrieder, J. R. & Machado, M. R.https://doi.org/10.1016/j.jjimei.2024.100234Design Implications for Integrating AI Chatbot Technology with Learning Management Systems: A Study-based Analysis on Perceived Benefits and Challenges in Higher Education (2024)In ICAITE 2024: Proceedings of the 2024 International Conference on Artificial Intelligence and Teacher Education (pp. 1-8). ACM Press. Sedrakyan, G., Borsci, S., Machado, M., Rogetzer, P. & Mes, M.https://doi.org/10.1145/3702386.3702405Applications of machine learning algorithms to support COVID-19 diagnosis using X-rays data information (2024)Expert systems with applications, 238(Part B). Article 122029. Medeiros, E. P., Machado, M. R., de Freitas, E. D. G., da Silva, D. S. & de Souza, R. W. R.https://doi.org/10.1016/j.eswa.2023.122029
2023
Challenge-Based Learning and Constructive Alignment: A Challenge for Information Systems' Educators (2023)In SEFI 2023 - 51st Annual Conference of the European Society for Engineering Education: Engineering Education for Sustainability, Proceedings (pp. 2517-2526). Societe Europeenne pour la Formation des Ingenieurs (SEFI). Moreira, J., Ugulino, W., Machado, M. R. & Pires, L. F.https://doi.org/10.21427/V8GC-C794Digital Finance: Reaching New Frontiers (2023)Open Research Europe, 3. Article 38. Osterrieder, J., Hadji Misheva, B. & Machado, M.https://doi.org/10.12688/openreseurope.15386.1
2022
Applying hybrid machine learning algorithms to assess customer risk-adjusted revenue in the financial industry (2022)Electronic commerce research and applications, 56. Article 101202. Machado, M. R. & Karray, S.https://doi.org/10.1016/j.elerap.2022.101202Integrating Customer Portfolio Theory and the Multiple Sources of Risk Approaches to Model Risk-Adjusted Revenue (2022)IFAC-papersonline, 55(16), 356-363. Machado, M. R. & Karray, S.https://doi.org/10.1016/j.ifacol.2022.09.050Assessing credit risk of commercial customers using hybrid machine learning algorithms (2022)Expert systems with applications, 200. Article 116889. Machado, M. R. & Karray, S.https://doi.org/10.1016/j.eswa.2022.116889
2019
LightGBM: An effective decision tree gradient boosting method to predict customer loyalty in the finance industry (2019)In 14th International Conference on Computer Science and Education, ICCSE 2019 (pp. 1111-1116). Article 8845529. IEEE. Machado, M. R., Karray, S. & De Sousa, I. T.https://doi.org/10.1109/ICCSE.2019.8845529An Agent-Based Model Applied to Brazilian Wind Energy Auctions (2019)IEEE Latin America transactions = Revista IEEE America Latina, 17(5), 865-874. Article 8891956. MacHado, M. R., Kenichi Fujii, M., De Oliveira Ribeiro, C. & Rego, E. E.https://doi.org/10.1109/TLA.2019.8891956